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arXiv · 2603.12762

TerraFlow: Multimodal, Multitemporal Representation Learning for Earth Observation

Nazar Puriy·Johannes Jakubik·Benedikt Blumenstiel·Konrad Schindler
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Abstract

We propose TerraFlow, a novel approach to multimodal, multitemporal learning for Earth observation. TerraFlow builds on temporal training objectives that enable sequence-aware learning across space, time, and modality, while remaining robust to the variable-length inputs commonly encountered in real-world Earth observation data. Our experiments demonstrate superiority of TerraFlow over state-of-the-art foundation models for Earth observation across all temporal tasks of the GEO-Bench-2 benchmark. We additionally demonstrate that TerraFlow is able to make initial steps towards deep-learning based risk map prediction for natural disasters -- a task on which other state-of-the-art foundation models frequently collapse. TerraFlow outperforms state-of-the-art foundation models by up to 50% in F1 score and 24% in Brier score.

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Nazar Puriy, Johannes Jakubik, Benedikt Blumenstiel, Konrad Schindler. 2026-03-13. TerraFlow: Multimodal, Multitemporal Representation Learning for Earth Observation. https://arxiv.org/abs/2603.12762

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